'''simple docstring'''




import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)

set_seed(7_7_0)


__UpperCAmelCase					={
    "c_attn": "att_proj",
    "c_proj": "out_proj",
    "c_fc": "in_proj",
    "transformer.": "",
    "h.": "layers.",
    "ln_1": "layernorm_1",
    "ln_2": "layernorm_2",
    "ln_f": "layernorm_final",
    "wpe": "position_embeds_layer",
    "wte": "input_embeds_layer",
}


__UpperCAmelCase					={
    "text_small": {
        "repo_id": "suno/bark",
        "file_name": "text.pt",
    },
    "coarse_small": {
        "repo_id": "suno/bark",
        "file_name": "coarse.pt",
    },
    "fine_small": {
        "repo_id": "suno/bark",
        "file_name": "fine.pt",
    },
    "text": {
        "repo_id": "suno/bark",
        "file_name": "text_2.pt",
    },
    "coarse": {
        "repo_id": "suno/bark",
        "file_name": "coarse_2.pt",
    },
    "fine": {
        "repo_id": "suno/bark",
        "file_name": "fine_2.pt",
    },
}

__UpperCAmelCase					=os.path.dirname(os.path.abspath(__file__))
__UpperCAmelCase					=os.path.join(os.path.expanduser("~"), ".cache")
__UpperCAmelCase					=os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=False							)      ->       List[Any]:
    __lowerCamelCase            =    model_type
    if use_small:
        key += "_small"
    return os.path.join(UpperCamelCase__							,    REMOTE_MODEL_PATHS[key]['''file_name''']							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    os.makedirs(UpperCamelCase__							,    exist_ok=UpperCamelCase__							)
    hf_hub_download(repo_id=UpperCamelCase__							,    filename=UpperCamelCase__							,    local_dir=UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=False							,    UpperCamelCase__="text"							)      ->       str:
    if model_type == "text":
        __lowerCamelCase            =    BarkSemanticModel
        __lowerCamelCase            =    BarkSemanticConfig
        __lowerCamelCase            =    BarkSemanticGenerationConfig
    elif model_type == "coarse":
        __lowerCamelCase            =    BarkCoarseModel
        __lowerCamelCase            =    BarkCoarseConfig
        __lowerCamelCase            =    BarkCoarseGenerationConfig
    elif model_type == "fine":
        __lowerCamelCase            =    BarkFineModel
        __lowerCamelCase            =    BarkFineConfig
        __lowerCamelCase            =    BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    __lowerCamelCase            =    f"""{model_type}_small""" if use_small else model_type
    __lowerCamelCase            =    REMOTE_MODEL_PATHS[model_key]
    if not os.path.exists(UpperCamelCase__							):
        logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""							)
        _download(model_info['''repo_id''']							,    model_info['''file_name''']							)
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location=UpperCamelCase__							)
    # this is a hack
    __lowerCamelCase            =    checkpoint['''model_args''']
    if "input_vocab_size" not in model_args:
        __lowerCamelCase            =    model_args['''vocab_size''']
        __lowerCamelCase            =    model_args['''vocab_size''']
        del model_args["vocab_size"]

    # convert Bark model arguments to HF Bark model arguments
    __lowerCamelCase            =    model_args.pop('''n_head'''							)
    __lowerCamelCase            =    model_args.pop('''n_embd'''							)
    __lowerCamelCase            =    model_args.pop('''n_layer'''							)

    __lowerCamelCase            =    ConfigClass(**checkpoint['''model_args''']							)
    __lowerCamelCase            =    ModelClass(config=UpperCamelCase__							)
    __lowerCamelCase            =    GenerationConfigClass()

    __lowerCamelCase            =    model_generation_config
    __lowerCamelCase            =    checkpoint['''model''']
    # fixup checkpoint
    __lowerCamelCase            =    '''_orig_mod.'''
    for k, v in list(state_dict.items()							):
        if k.startswith(UpperCamelCase__							):
            # replace part of the key with corresponding layer name in HF implementation
            __lowerCamelCase            =    k[len(UpperCamelCase__							) :]
            for old_layer_name in new_layer_name_dict:
                __lowerCamelCase            =    new_k.replace(UpperCamelCase__							,    new_layer_name_dict[old_layer_name]							)

            __lowerCamelCase            =    state_dict.pop(UpperCamelCase__							)

    __lowerCamelCase            =    set(state_dict.keys()							) - set(model.state_dict().keys()							)
    __lowerCamelCase            =    {k for k in extra_keys if not k.endswith('''.attn.bias'''							)}
    __lowerCamelCase            =    set(model.state_dict().keys()							) - set(state_dict.keys()							)
    __lowerCamelCase            =    {k for k in missing_keys if not k.endswith('''.attn.bias'''							)}
    if len(UpperCamelCase__							) != 0:
        raise ValueError(f"""extra keys found: {extra_keys}"""							)
    if len(UpperCamelCase__							) != 0:
        raise ValueError(f"""missing keys: {missing_keys}"""							)
    model.load_state_dict(UpperCamelCase__							,    strict=UpperCamelCase__							)
    __lowerCamelCase            =    model.num_parameters(exclude_embeddings=UpperCamelCase__							)
    __lowerCamelCase            =    checkpoint['''best_val_loss'''].item()
    logger.info(f"""model loaded: {round(n_params/1E6							,    1							)}M params, {round(UpperCamelCase__							,    3							)} loss"""							)
    model.eval()
    model.to(UpperCamelCase__							)
    del checkpoint, state_dict

    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=False							,    UpperCamelCase__="text"							)      ->       Optional[Any]:
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    __lowerCamelCase            =    '''cpu'''  # do conversion on cpu

    __lowerCamelCase            =    _get_ckpt_path(UpperCamelCase__							,    use_small=UpperCamelCase__							)
    __lowerCamelCase            =    _load_model(UpperCamelCase__							,    UpperCamelCase__							,    model_type=UpperCamelCase__							,    use_small=UpperCamelCase__							)

    # load bark initial model
    __lowerCamelCase            =    _bark_load_model(UpperCamelCase__							,    '''cpu'''							,    model_type=UpperCamelCase__							,    use_small=UpperCamelCase__							)

    if model_type == "text":
        __lowerCamelCase            =    bark_model['''model''']

    if model.num_parameters(exclude_embeddings=UpperCamelCase__							) != bark_model.get_num_params():
        raise ValueError('''initial and new models don\'t have the same number of parameters'''							)

    # check if same output as the bark model
    __lowerCamelCase            =    5
    __lowerCamelCase            =    10

    if model_type in ["text", "coarse"]:
        __lowerCamelCase            =    torch.randint(2_56							,    (batch_size, sequence_length)							,    dtype=torch.int							)
        __lowerCamelCase            =    bark_model(UpperCamelCase__							)[0]

        __lowerCamelCase            =    model(UpperCamelCase__							)

        # take last logits
        __lowerCamelCase            =    output_new_model_total.logits[:, [-1], :]

    else:
        __lowerCamelCase            =    3
        __lowerCamelCase            =    8
        __lowerCamelCase            =    torch.randint(2_56							,    (batch_size, sequence_length, n_codes_total)							,    dtype=torch.int							)

        __lowerCamelCase            =    model(UpperCamelCase__							,    UpperCamelCase__							)
        __lowerCamelCase            =    bark_model(UpperCamelCase__							,    UpperCamelCase__							)

        __lowerCamelCase            =    output_new_model_total.logits

    # output difference should come from the difference of self-attention implementation design
    if output_new_model.shape != output_old_model.shape:
        raise ValueError('''initial and new outputs don\'t have the same shape'''							)
    if (output_new_model - output_old_model).abs().max().item() > 1E-3:
        raise ValueError('''initial and new outputs are not equal'''							)

    Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
    model.save_pretrained(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    )      ->       Optional[Any]:
    __lowerCamelCase            =    os.path.join(UpperCamelCase__							,    UpperCamelCase__							)

    __lowerCamelCase            =    BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase__							,    '''config.json'''							)							)
    __lowerCamelCase            =    BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase__							,    '''config.json'''							)							)
    __lowerCamelCase            =    BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase__							,    '''config.json'''							)							)
    __lowerCamelCase            =    EncodecConfig.from_pretrained('''facebook/encodec_24khz'''							)

    __lowerCamelCase            =    BarkSemanticModel.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    BarkCoarseModel.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    BarkFineModel.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    EncodecModel.from_pretrained('''facebook/encodec_24khz'''							)

    __lowerCamelCase            =    BarkConfig.from_sub_model_configs(
        UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)

    __lowerCamelCase            =    BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config							,    coarseAcoustic.generation_config							,    fineAcoustic.generation_config							)

    __lowerCamelCase            =    BarkModel(UpperCamelCase__							)

    __lowerCamelCase            =    semantic
    __lowerCamelCase            =    coarseAcoustic
    __lowerCamelCase            =    fineAcoustic
    __lowerCamelCase            =    codec

    __lowerCamelCase            =    bark_generation_config

    Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
    bark.save_pretrained(UpperCamelCase__							,    repo_id=UpperCamelCase__							,    push_to_hub=UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  # Required parameters

  parser.add_argument("model_type", type=str, help="text, coarse or fine.")
  parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")

  __UpperCAmelCase					=parser.parse_args()

  load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase__							)							,    UpperCamelCase__							)
    return number - int(UpperCamelCase__							)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))
